Author: Denis Avetisyan
A decade-long study of top conferences reveals diverging trajectories in size and influence, challenging established hierarchies in the field.
Quantitative analysis of seven leading NLP/AI conferences demonstrates decoupling between scale and scientific impact, raising questions about peer review and the growth of large language model research.
The recent explosion of large language models has spurred growth in NLP research, yet raised concerns about maintaining conference quality alongside increasing submissions. Our study, ‘Has ACL Lost Its Crown? A Decade-Long Quantitative Analysis of Scale and Impact Across Leading AI Conferences’, addresses this by providing a ten-year, cross-venue analysis of seven major conferences using a novel bibliometric framework. We find diverging trends-stable impact for machine learning venues, stratification and mixed efficiency in NLP, and structural decline in broader AI conferences-suggesting a decoupling between scale and influence. Will these trends solidify, reshaping the landscape of impactful research dissemination in artificial intelligence?
The Shifting Sands of Scholarly Influence
While academic conferences remain the cornerstone of knowledge dissemination, simply increasing the number of presented papers doesn’t guarantee a corresponding rise in research influence. Studies reveal a complex relationship between conference participation and lasting impact, suggesting that the sheer volume of presentations can overshadow the quality and subsequent visibility of individual studies. The current landscape often prioritizes breadth of output over depth of engagement, leading to a situation where numerous presentations receive limited attention and fail to translate into significant citations or broader scholarly recognition. This highlights a crucial need to reassess how scholarly contributions are evaluated, moving beyond simple counts of conference appearances toward metrics that better reflect genuine intellectual contribution and lasting influence within a field.
While average citation counts have long served as a benchmark for scholarly influence, this metric often obscures crucial nuances in research quality and impact. A consistently rising average can mask a decline in the influence of individual highly-cited works, as the sheer volume of new publications dilutes attention across a larger pool of research. Furthermore, this metric fails to capture the diverse ways scholarship exerts influence – from informing policy decisions and driving innovation to shaping public discourse – beyond the confines of academic citation. Consequently, relying solely on average citations provides an incomplete, and potentially misleading, picture of the evolving landscape of scholarly impact, prompting a need for more sophisticated evaluative approaches that account for the context, reach, and lasting significance of research contributions.
Contemporary scholarship faces a noteworthy challenge: the rapid increase in published research isn’t necessarily mirrored by a comparable surge in impactful findings. While the sheer volume of scientific output has expanded exponentially in recent decades, assessing whether this proliferation translates to meaningful advancements remains a critical question. Studies reveal a potential decoupling between publication quantity and qualitative influence, suggesting that the ‘impact factor’ – a traditional metric – may be becoming increasingly diluted. This raises concerns about the efficiency of knowledge dissemination and the ability to identify truly groundbreaking work amidst a growing sea of publications, prompting researchers to explore more nuanced methods for evaluating scholarly contributions beyond simple citation counts and publication numbers.
Research impact isn’t evenly distributed across the scholarly landscape; instead, a remarkably small percentage of publications consistently garner the vast majority of citations. This phenomenon, often described as a power-law distribution, reveals that a select few papers – perhaps those presenting groundbreaking discoveries or synthesizing existing knowledge in novel ways – become disproportionately influential, while the majority receive comparatively little attention. Analyses demonstrate that the most highly cited papers often account for an overwhelming share of all citations within a field, suggesting that impact isn’t simply additive – more papers don’t necessarily equate to broader influence. This skewed distribution has significant implications for evaluating research quality, as relying solely on average citation counts can mask the concentration of influence and potentially undervalue important, yet less-cited, work. Understanding this pattern is crucial for developing more nuanced metrics and fostering a more accurate assessment of scholarly contributions.
Quality-Quantity Elasticity: Measuring the Strain
Quality-Quantity Elasticity (QQE) is defined as a quantitative metric designed to evaluate the correlation between the growth of a conference, measured by the number of accepted papers, and its corresponding scholarly impact. Specifically, QQE aims to determine the extent to which an increase in the quantity of published papers is associated with a commensurate increase in the quality of research, as indicated by citation rates. The metric is calculated by assessing the ratio of the Citation Growth Rate to the Publication Growth Rate for a given conference over a specified period. A QQE value greater than one suggests that the conference is experiencing impact growth at a rate exceeding its publication growth, while a value less than one indicates the opposite. This allows for a direct comparison of conferences regarding their efficiency in translating growth into scholarly influence.
Quality-Quantity Elasticity (QQE) is calculated as the ratio of the Citation Growth Rate to the Publication Growth Rate for a given conference. This provides a direct measure of impact gained per unit of growth in accepted papers; a QQE value of 1 indicates that each additional accepted paper corresponds to a proportional increase in citations. Values greater than 1 suggest increasing returns – impact is growing faster than the number of papers – while values less than 1 indicate diminishing returns or that conference growth is not translating effectively into increased scholarly impact. The metric is expressed as a unitless number, allowing for comparison across conferences with differing scales of publication and citation activity.
Analysis of Quality-Quantity Elasticity (QQE) trends allows for differentiation between conferences experiencing concurrent growth in submissions and scholarly influence, and those where increased scale does not correlate with heightened impact. A rising QQE value indicates that a conference is maintaining or improving its impact relative to its growth in accepted papers, suggesting effective peer review and a focus on high-quality submissions. Conversely, a declining QQE value suggests that the conference’s growth is not translating into proportional increases in citations or other measures of scholarly impact, potentially indicating a dilution of quality due to increased volume. Tracking QQE over time provides a quantitative basis for assessing the health and trajectory of a conference’s scholarly contribution.
Quality-Quantity Elasticity (QQE) calculation necessitates precise tracking of two key rates for each conference: Publication Growth Rate and Citation Growth Rate. Publication Growth Rate is determined by the annual percentage change in the number of accepted papers, calculated as $ \frac{P_t – P_{t-1}}{P_{t-1}} \times 100$, where $P_t$ represents the number of papers accepted in year $t$. Similarly, Citation Growth Rate is calculated as the annual percentage change in the total number of citations received by papers accepted in the prior year, expressed as $ \frac{C_t – C_{t-1}}{C_{t-1}} \times 100$, where $C_t$ represents the total citations in year $t$. Accurate data collection for both metrics, including comprehensive paper and citation indexing, is fundamental to obtaining reliable QQE values and ensuring meaningful comparisons between conferences.
Conference Performance: Divergent Ecosystems
In 2023, the Conference on Neural Information Processing Systems (NeurIPS) exhibited a strong correlation between its growth in submissions and its research impact, as measured by its Quality-adjusted Citation Equivalent (QQE) score. Despite substantial increases in the number of accepted papers, NeurIPS maintained a high QQE of 3.04, indicating efficient scale-impact coupling. This suggests that the conference’s review processes and selection criteria effectively preserved research quality even with increased volume, a characteristic differentiating it from other large-scale AI conferences experiencing decoupling between growth and impact.
The North American Chapter of the Association for Computational Linguistics (NAACL) currently demonstrates a high density of impactful research relative to its conference size, as evidenced by a Normalized H-index of 7.04. This metric indicates a substantial concentration of highly cited publications within the conference’s total output. However, recent data suggests a potential shift in this relationship, with emerging signals indicating a possible decoupling between conference growth and sustained research impact. Further monitoring is required to confirm whether this trend represents a fundamental change in NAACL’s performance characteristics.
The Association for Computational Linguistics (ACL) conference has experienced substantial growth in publication volume, with a 536% increase in accepted papers between 2015 and 2024. However, this expansion does not correlate with a proportional increase in research impact. Analysis indicates a decoupling between publication growth and key impact metrics, suggesting that the conference is accepting a larger volume of papers without a corresponding rise in overall research influence or citation rates. This trend differentiates ACL from conferences like NeurIPS, which maintain strong scale-impact coupling, and signals a potential shift in the conference’s quality-to-volume ratio.
From 2015 to 2024, the Empirical Methods in Natural Language Processing (EMNLP) conference experienced a 400% increase in the number of accepted papers. However, this rapid expansion has coincided with a demonstrable decline in publication quality, as evidenced by a decreasing Quality-adjusted Quantity Estimate (QQE). This suggests a potential decoupling of conference size and overall impact, indicating that the increased volume of submissions has not been matched by a proportional increase in the quality of accepted research.
Beyond the Leaders: Assessing Broader Systemic Strain
Recent analyses reveal that both the Association for the Advancement of Artificial Intelligence (AAAI) and the International Joint Conference on Artificial Intelligence (IJCAI) are facing considerable structural challenges, evidenced by increasingly volatile performance metrics. This isn’t simply a matter of fluctuating submission numbers; rather, indicators suggest a weakening of the conferences’ core foundations. Specifically, measures of research impact and citation rates have become less consistent, signaling potential issues with maintaining quality control as submissions increase. This instability raises concerns about the long-term health of these historically prominent venues and their ability to reliably identify and promote genuinely groundbreaking contributions to the field of artificial intelligence. The observed trends underscore a need for critical evaluation and potential restructuring to ensure these conferences remain vital hubs for impactful research and scholarly exchange.
Despite remaining a highly influential venue for machine learning research, the International Conference on Learning Representations (ICLR) is exhibiting signs of approaching a growth plateau. Recent analyses indicate that the rate of submission and acceptance increases has begun to decelerate, suggesting the conference may be nearing a saturation point in terms of attracting novel and impactful contributions. While ICLR continues to publish cutting-edge work, this trend implies a shift in the dynamics of the conference and highlights the increasing challenge of maintaining consistent growth amidst a rapidly expanding field. This isn’t necessarily indicative of decline, but rather a maturation process where sustaining impact requires greater selectivity and a focus on truly groundbreaking research to differentiate itself from the increasing number of specialized workshops and alternative publication venues.
Recent analysis of top-tier artificial intelligence conferences reveals a concerning decoupling between conference growth and research quality, indicating that simply increasing the volume of submissions and accepted papers doesn’t necessarily translate to advancements in the field. While conferences like NeurIPS have experienced substantial expansion, metrics suggest this growth hasn’t been matched by a proportional increase in the significance or novelty of presented research. This trend underscores a critical need for robust peer review processes and a renewed focus on curating impactful contributions, rather than prioritizing sheer quantity. Maintaining a high standard of research-ensuring that accepted papers genuinely push the boundaries of knowledge-is paramount to the long-term health and credibility of academic conferences and the broader scientific community they serve. A failure to address this decoupling could lead to a saturation of mediocre research, hindering genuine innovation and obscuring truly valuable contributions.
A noteworthy decline in Quality-adjusted Quantity of Publications (QQE) – exemplified by the -18.51% drop observed for the Association for Computational Linguistics (ACL) conference in 2021 – suggests potential vulnerabilities within the peer review system. This metric, which balances publication volume with citation impact, indicates that even highly-regarded conferences may be experiencing challenges in consistently selecting genuinely impactful research. Such a decrease doesn’t necessarily imply a reduction in overall research quality, but rather raises questions about the efficacy of current evaluation methods and the potential for increased noise – the publication of less significant work – within the academic literature. Maintaining a robust peer review process is therefore crucial, not just for upholding the prestige of conferences, but also for ensuring the reliable dissemination of high-quality knowledge and guiding future research directions.
The study’s observation of diverging trajectories across AI conferences-where scale doesn’t necessarily correlate with impact-resonates with a fundamental truth about complex systems. It isn’t about imposing order, but acknowledging the inherent unpredictability. As Ada Lovelace noted, “The Analytical Engine has no pretensions whatever to originate anything.” This isn’t a limitation, but rather a recognition that even the most sophisticated systems-be they mechanical engines or scientific conferences-operate within constraints. The decoupling of size and influence observed isn’t a failure of peer review, but a natural consequence of growth; stability is merely an illusion that caches well. Chaos isn’t failure-it’s nature’s syntax.
The Shifting Sands
The observation of diverging trajectories across these conferences is not a judgement, but a description. Scale, it seems, does not guarantee resonance. The pursuit of ever-larger venues and submission numbers feels increasingly like rearranging deck chairs. One wonders if the true measure of a conference lies not in its breadth, but in the density of genuine advancement-a metric far more resistant to quantification. The study highlights a decoupling, but fails to predict its ultimate form. Will influence concentrate in smaller, more curated gatherings? Or will the current trend simply accelerate, creating ever-larger events with diminishing returns on intellectual capital?
The reliance on citation analysis, while pragmatic, remains a flawed proxy for impact. Ideas gestate, influence ripples outward through channels unseen by bibliometrics. A paper rejected today may seed a revolution tomorrow. These conferences are not pipelines, but ecosystems, and ecosystems defy neat categorization. The focus on ‘quality’-as defined by acceptance rates and citation counts-risks becoming self-fulfilling, rewarding incrementalism over true novelty.
Ultimately, the architecture of these gatherings-the submission processes, the review criteria, the very notion of a ‘leading’ conference-is a compromise frozen in time. Technologies change, dependencies remain. The field moves on, regardless of attempts to chart its course. The question isn’t whether ACL will ‘lose its crown,’ but whether the very concept of a ‘crown’ holds any meaning in a landscape defined by constant, unpredictable evolution.
Original article: https://arxiv.org/pdf/2512.04448.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-06 05:00